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http://dx.doi.org/10.13089/JKIISC.2017.27.6.1499

POMDP Based Trustworthy Android App Recommendation Services  

Oh, Hayoung (Ajou University)
Goo, EunHee (Ajou University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.27, no.6, 2017 , pp. 1499-1506 More about this Journal
Abstract
The use of smartphones and the launch of various apps have increased exponentially, and malicious apps have also increased. Existing app recommendation systems have been limited to operate based on static information analysis such as ratings, comments, and popularity categories of other users who are online. In this paper, we first propose a robust app recommendation system that realistically uses dynamic information of apps actually used in smartphone and considers static information and dynamic information at the same time. In other words, this paper proposes a robust Android app recommendation system by partially reflecting the time of the app, the frequency of use of the app, the interaction between the app and the app, and the number of contact with the Android kernel. As a result of the performance evaluation, the proposed method proved to be a robust and efficient app recommendation system.
Keywords
Partially Observable Markov Decision Process (POMDP) based Recommender System; prediction shift;
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1 G. Linden, B. Smith, and J. York, "Amazon. com recommendations: Item-to-item collaborative filtering," Internet Computing, IEEE, pp. 76-80, vol. 7, no. 1, Jan. 2003.
2 Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi and Zhi Wang, " Friendbook: A Semantic-Based Friend Recommendation System for Social Networks," Mobile Computing, IEEE Transactions on, pp. 538-551, vol. 14, no. 3, Mar. 2015.   DOI
3 Sumedh Sawantand Gina Pai, "Yelp Food Recommendation System," Stanford University, pp. 1-5, Jan. 2013.
4 G. Noh, Y.-m. Kang, H. Oh, and C.-k. Kim, "Robust Sybil attack defense with information level in online Recommender Systems," Expert Systems with Applications, pp. 1781-1791, vol. 41, no. 4, Mar. 2014.   DOI
5 Paarijaat Aditya et al., "EnCore: Private, Context-based Communication for Mobile Social Apps," MobiSys '14 Proceedings of the 12th annual international conference on Mobile systems, applications, and services, pp. 135-148, June.2014.
6 Erika Chin et al., "Analyzing Inter-Application Communication in Android," MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services, pp. 239-252, July. 2011.
7 Bo Yan and Guanling Chen, "AppJoy: Personalized Mobile Application Discovery," MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services, pp. 113-126, July. 2011.
8 B. Mobasher, R. Burke, R. Bhaumik, and C. Williams, "Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness," Journal ACM Transactions on Internet Technology (TOIT), pp. 1-41, vol. 7, no. 4, Oct. 2007.   DOI
9 Z. Cheng and N. Hurley, "Robust collaborative recommendation by least trimmed squares matrix factorization," in 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 105-112, Oct. 2010.
10 Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, no. 8, pp. 3-37, Aug. 2009.   DOI